Instructions to use ArtemisTAO/ML60 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArtemisTAO/ML60 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ArtemisTAO/ML60")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ArtemisTAO/ML60") model = AutoModelForCausalLM.from_pretrained("ArtemisTAO/ML60") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ArtemisTAO/ML60 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ArtemisTAO/ML60" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArtemisTAO/ML60", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ArtemisTAO/ML60
- SGLang
How to use ArtemisTAO/ML60 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ArtemisTAO/ML60" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArtemisTAO/ML60", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ArtemisTAO/ML60" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArtemisTAO/ML60", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ArtemisTAO/ML60 with Docker Model Runner:
docker model run hf.co/ArtemisTAO/ML60
Upload folder using huggingface_hub
Browse files- README.md +40 -0
- config.json +31 -0
- mergekit_config.yml +8 -0
- model.safetensors +3 -0
README.md
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---
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base_model:
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- memevis/supp6
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- memevis/bl0
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library_name: transformers
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tags:
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- mergekit
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- merge
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---
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# merge
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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### Merge Method
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This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
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### Models Merged
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The following models were included in the merge:
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* [memevis/supp6](https://huggingface.co/memevis/supp6)
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* [memevis/bl0](https://huggingface.co/memevis/bl0)
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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models:
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- model: memevis/supp6
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- model: memevis/bl0
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merge_method: slerp
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base_model: memevis/bl0
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dtype: bfloat16
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parameters:
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t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
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```
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 2048,
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"max_position_embeddings": 4096,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 16,
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"pad_token_id": 100257,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.1",
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"unsloth_version": "2025.3.19",
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"use_cache": false,
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"vocab_size": 100263
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}
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mergekit_config.yml
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models:
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- model: memevis/supp6
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- model: memevis/bl0
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merge_method: slerp
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base_model: memevis/bl0
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dtype: bfloat16
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parameters:
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t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:616b62a89060ccaf4e47faee4af5080817e0ea2b1ed088d62b50e1bf3a049c39
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size 914119032
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